Interleukin-6 (IL-6) modulates immune response, estrogen production and growth pathways in breast cancer. We evaluated the effect of several common, functional IL-6 promoter variants in node-positive breast cancer patients enrolled on a multicenter, cooperative group, adjuvant chemotherapy trial to determine whether these variants were associated with clinical outcome overall and by estrogen-receptor tumor phenotype.
Genomic DNA and clinical data were collected from a clinical trial of adjuvant anthracycline-based chemotherapy followed by randomization to high-dose cyclophosphamide/thiotepa or observation (INT-0121). Genotyping for -174G>C (rs1800795), -597G>A (rs1800797) and -572G>C (rs1800796) was performed by site-specific PCR and PyroSequencing, while the -373AnTn repeat was directly sequenced. Log-rank tests and Cox modeling were used to compare outcomes by genotype/haplotype and other factors.
346 patients (64% of trial) had corresponding genotype/clinical data available and did not differ from overall trial participants. After adjustment, patients with ER positive tumors and genotypes 597GG or 174GG had significantly worse disease-free survival (DFS) (HR 1.6, p=0.02 and HR 1.71, p=0.007, respectively), while the 373 8A12T repeat appeared to be protective (HR 0.62, p=0.02). The presence of at least one copy of the haplotype [-597G;-572G;-373[10A/11T];-174G]) was associated with worse DFS (HR 1.46, p=0.04). Kaplan-Meier plots show that all patients in this group relapsed by 24 months from diagnosis. This poor risk haplotype was quite common overall (estimated frequency 0.20) and twice as frequent among Blacks (estimated frequency 0.41).
Breast Cancer; Polymorphism; Cytokines; Interleukin-6
Mammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold.
We recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or ductal carcinoma in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer.
In the multivariate model, age (odds ratio (OR) = 1.05; 95% confidence interval (CI), 1.03 to 1.08; P < 0.001), SNP panel relative risk (OR = 2.30; 95% CI, 1.06 to 4.99, P = 0.035) and BMI (≥30 kg/m2 versus <25 kg/m2; OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women (OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) had more than twice the odds of cancer diagnosis compared to women with a BMI <25 kg/m2. The SNP panel appeared to have predictive ability among both white and black women.
Breast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.
Electronic supplementary material
The online version of this article (doi:10.1186/s13058-014-0509-4) contains supplementary material, which is available to authorized users.
While there has been extensive research developing gene-environment interaction (GEI) methods in case-control studies, little attention has been given to sparse and efficient modeling of GEI in longitudinal studies. In a two-way table for GEI with rows and columns as categorical variables, a conventional saturated interaction model involves estimation of a specific parameter for each cell, with constraints ensuring identifiability. The estimates are unbiased but are potentially inefficient because the number of parameters to be estimated can grow quickly with increasing categories of row/column factors. On the other hand, Tukey’s one degree of freedom (df) model for non-additivity treats the interaction term as a scaled product of row and column main effects. Due to the parsimonious form of interaction, the interaction estimate leads to enhanced efficiency and the corresponding test could lead to increased power. Unfortunately, Tukey’s model gives biased estimates and low power if the model is misspecified. When screening multiple GEIs where each genetic and environmental marker may exhibit a distinct interaction pattern, a robust estimator for interaction is important for GEI detection. We propose a shrinkage estimator for interaction effects that combines estimates from both Tukey’s and saturated interaction models and use the corresponding Wald test for testing interaction in a longitudinal setting. The proposed estimator is robust to misspecification of interaction structure. We illustrate the proposed methods using two longitudinal studies — the Normative Aging Study and the Multi-Ethnic Study of Atherosclerosis.
adaptive shrinkage estimation; gene-environment interaction; longitudinal data; Tukey’s one df test for non-additivity
With the number of sequenced plant genomes growing, the number of predicted genes and functional annotations is also increasing. The association between genes and phenotypic traits is currently of great interest. Unfortunately, the information available today is widely scattered over a number of different databases. Information retrieval (IR) has become an all-encompassing bioinformatics methodology for extracting knowledge from complex, heterogeneous and distributed databases, and therefore can be a useful tool for obtaining a comprehensive view of plant genomics, from genes to traits. Here we describe LAILAPS (http://lailaps.ipk-gatersleben.de), an IR system designed to link plant genomic data in the context of phenotypic attributes for a detailed forward genetic research. LAILAPS comprises around 65 million indexed documents, encompassing >13 major life science databases with around 80 million links to plant genomic resources. The LAILAPS search engine allows fuzzy querying for candidate genes linked to specific traits over a loosely integrated system of indexed and interlinked genome databases. Query assistance and an evidence-based annotation system enable time-efficient and comprehensive information retrieval. An artificial neural network incorporating user feedback and behavior tracking allows relevance sorting of results. We fully describe LAILAPS’s functionality and capabilities by comparing this system’s performance with other widely used systems and by reporting both a validation in maize and a knowledge discovery use-case focusing on candidate genes in barley.
Functional gene annotation; Information retrieval; Integrative search engine; Plant genomics resources; Traits
A meta-analysis was performed to evaluate the efficacy and safety of nerve growth factor (NGF) in the treatment of Bell’s palsy. PubMed, the Cochrane Central Register of Controlled Trials, Embase and a number of Chinese databases, including the China National Knowledge Infrastructure, China Biology Medicine disc, VIP Database for Chinese Technical Periodicals and Wan Fang Data, were used to collect randomised controlled trials (RCTs) of NGF for Bell’s palsy. The span of the search covered data from the date of database establishment until December 2013. The included trials were screened comprehensively and rigorously. The efficacies of NGF were pooled via meta-analysis performed using Review Manager 5.2 software. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using the fixed-effects model. The meta-analysis of eight RCTs showed favorable effects of NGF on the disease response rate (n=642; OR, 3.87; 95% CI, 2.13–7.03; P<0.01; I2=0%). However, evidence supporting the effectiveness of NGF for the treatment of Bell’s palsy is limited. The number and quality of trials are too low to form solid conclusions. Further meticulous RCTs are required to overcome the limitations identified in the present study.
nerve growth factor; Bell’s palsy; meta-analysis
Adiponectin has been indicated to be linked with depression. In the present study, a meta-analysis was performed to evaluate the association between adiponectin levels and depression. Six studies with a total of 4,220 subjects were selected for inclusion in the analysis. The references were retrieved via PubMed, Cochrane Central Register of Controlled Trials and Embase, and the following Chinese databases: The China National Knowledge Infrastructure, China Biology Medicine disc, VIP Database for Chinese Technical Periodicals and Wan Fang Data. The analyses were performed using Review Manager 5.2 software. The standardized mean difference (SMD) with 95% confidence intervals (CIs) was assessed following pooling the collected data for analysis. A significant association was detected between adiponectin levels and depression in European populations. In the European group of patients with depression, improvements were observed in adiponectin levels (SMD, −5.00 µg/ml, 95% CI, −7.13 to −2.88). The current meta-analysis indicates that patients with patients had a lower adiponectin level when compared to healthy patients in European groups.
adiponectin; depression; meta-analysis
In Parkinson's disease (PD), neuronal cells undergo mitotic catastrophe and endoreduplication prior to cell death; however, the regulatory mechanisms remain to be defined. In this study, we investigated cell cycle regulation of DNA polymerase β (poly β) in rotenone-based dopaminergic cellular and animal models. Incubation with a low concentration (0.25 µM) of rotenone for 1.5 to 7 days resulted in a flattened cell body and decreased DNA replication during S phase, whereas a high concentration (2 µM) of rotenone exposure resulted in enlarged, multi-nucleated cells and converted the mitotic cycle into endoreduplication. Consistently, DNA poly β, which is mainly involved in DNA repair synthesis, was upregulated to a high level following exposure to 2 µM rotenone. The abrogation of DNA poly β by siRNA transfection or dideoxycytidine (DDC) treatment attenuated the rotenone-induced endoreduplication. The cell cycle was reactivated in cyclin D-expressing dopaminergic neurons from the substantia nigra (SN) of rats following stereotactic (ST) infusion of rotenone. Increased DNA poly β expression was observed in the substantia nigra pars compacta (SNc) and the substantia nigra pars reticulate (SNr) of rotenone-treated rats. Collectively, in the in vitro model of rotenone-induced mitotic catastrophe, the overexpression of DNA poly β promotes endoreduplication; in the in vivo model, the upregulation of DNA poly β and cell cycle reentry were also observed in the adult rat substantia nigra. Therefore, the cell cycle regulation of DNA poly β may be involved in the pathological processes of PD, which results in the induction of endoreduplication.
Accurate assessment of a woman’s risk to develop specific subtypes of breast cancer is critical for appropriate utilization of chemopreventative measures, such as with tamoxifen in preventing estrogen-receptor positive breast cancer. In this context, we investigate quantitative measures of breast density and parenchymal texture, measures of glandular tissue content and tissue structure, as risk factors for estrogen-receptor positive (ER+) breast cancer. Mediolateral oblique (MLO) view digital mammograms of the contralateral breast from 106 women with unilateral invasive breast cancer were retrospectively analyzed. Breast density and parenchymal texture were analyzed via fully-automated software. Logistic regression with feature selection and was performed to predict ER+ versus ER− cancer status. A combined model considering all imaging measures extracted was compared to baseline models consisting of density-alone and texture-alone features. Area under the curve (AUC) of the receiver operating characteristic (ROC) and Delong’s test were used to compare the models’ discriminatory capacity for receptor status. The density-alone model had a discriminatory capacity of 0.62 AUC (p=0.05). The texture-alone model had a higher discriminatory capacity of 0.70 AUC (p=0.001), which was not significantly different compared to the density-alone model (p=0.37). In contrast the combined density-texture logistic regression model had a discriminatory capacity of 0.82 AUC (p<0.001), which was statistically significantly higher than both the density-alone (p<0.001) and texture-alone regression models (p=0.04). The combination of breast density and texture measures may have the potential to identify women specifically at risk for estrogen-receptor positive breast cancer and could be useful in triaging women into appropriate risk-reduction strategies.
Digital Mammography; Breast Percent Density (PD%); Parenchymal Texture; Breast Cancer Risk; Receptor
The life-science community faces a major challenge in handling “big data”, highlighting the need for high quality infrastructures capable of sharing and publishing research data. Data preservation, analysis, and publication are the three pillars in the “big data life cycle”. The infrastructures currently available for managing and publishing data are often designed to meet domain-specific or project-specific requirements, resulting in the repeated development of proprietary solutions and lower quality data publication and preservation overall.
e!DAL is a lightweight software framework for publishing and sharing research data. Its main features are version tracking, metadata management, information retrieval, registration of persistent identifiers (DOI), an embedded HTTP(S) server for public data access, access as a network file system, and a scalable storage backend. e!DAL is available as an API for local non-shared storage and as a remote API featuring distributed applications. It can be deployed “out-of-the-box” as an on-site repository.
e!DAL was developed based on experiences coming from decades of research data management at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK). Initially developed as a data publication and documentation infrastructure for the IPK’s role as a data center in the DataCite consortium, e!DAL has grown towards being a general data archiving and publication infrastructure. The e!DAL software has been deployed into the Maven Central Repository. Documentation and Software are also available at: http://edal.ipk-gatersleben.de.
Research data management; Data publication; Persistent identifier; Metadata annotation; Shared repositories; JAVA API
Many existing cohorts with longitudinal data on environmental exposures, occupational history, lifestyle/behavioral characteristics and health outcomes have collected genetic data in recent years. In this paper, we consider the problem of modeling gene-gene, gene-environment interactions with repeated measures data on a quantitative trait. We review possibilities of using classical models proposed by Tukey (1949) and Mandel (1961) using the cell means of a two-way classification array for such data. Whereas these models are effective for detecting interactions in presence of main effects, they fail miserably if the interaction structure is misspecified. We explore a more robust class of interaction models that are based on a singular value decomposition of the cell means residual matrix after fitting the additive main effect terms. This class of additive main effects and multiplicative interaction (AMMI) models (Gollob, 1968) provide useful summaries for subject-specific and time-varying effects as represented in terms of their contribution to the leading eigenvalues of the interaction matrix. It also makes the interaction structure more amenable to geometric representation. We call this analysis “Principal Interactions Analysis” (PIA). While the paper primarily focusses on a cell-mean based analysis of repeated measures outcome, we also introduce resampling-based methods that appropriately recognize the unbalanced and longitudinal nature of the data instead of reducing the response to cell-means. The proposed methods are illustrated by using data from the Normative Aging Study, a longitudinal cohort study of Boston area veterans since 1963. We carry out simulation studies under an array of classical interaction models and common epistasis models to illustrate the properties of the PIA procedure in comparison to the classical alternatives.
biplot; column interaction; eigenvalue; epistasis; intraclass correlation; likelihood-ratio test; non-additivity; permutation tests; pseudo F-test; row interaction; singular vector; Wishart matrix
Laboratory studies suggest that vitamin D (vitD) enhances chemotherapy-induced cell death. The objective of this study was to determine whether pretreatment vitD levels were associated with response to neoadjuvant chemotherapy (NACT) in women with breast cancer. Study patients (n = 82) were enrolled on the I-SPY TRIAL, had HER2-negative tumors, and available pretreatment serum. VitD levels were measured via DiaSorin radioimmunoassay. The primary outcome was pathologic residual cancer burden (RCB; dichotomized 0/1 vs. 2/3). Secondary outcomes included biomarkers of proliferation, differentiation, and apoptosis (Ki67, grade, Bcl2, respectively) and 3-year relapse-free survival (RFS). Mean and median vitD values were 22.7 ng/mL (SD 11.9) and 23.1 ng/mL, respectively; 72% of patients had levels deemed “insufficient” (<30 ng/mL) by the Institute of Medicine (IOM). VitD level was not associated with attaining RCB 0/1 after NACT (univariate odds ratio [OR], 1.01; 95% CI, 0.96–1.05) even after adjustment for hormone receptor status (HR), grade, Ki67, or body mass index (BMI). Lower vitD levels were associated with higher tumor Ki67 adjusting for race (OR, 0.95; 95% CI, 0.90–0.99). VitD level was not associated with 3-year RFS, either alone (hazard ratio [HzR], 0.98; 95% CI, 0.95–1.02) or after adjustment for HR, grade, Ki-67, BMI, or response. VitD insufficiency was common at the time of breast cancer diagnosis among women who were candidates for NACT and was associated with a more proliferative phenotype. However, vitD levels had no impact on tumor response to NACT or short-term prognosis.
Breast cancer; neoadjuvant chemotherapy; response; vitamin D
Rationale and Objectives
Parenchymal texture patterns have been previously associated with breast cancer risk. Yet, their underlying biological determinants remain poorly understood. Here, we investigate the potential of mammographic parenchymal texture as a phenotypic imaging marker of endogenous hormonal exposure.
Materials and Methods
A retrospective cohort study was performed. Digital mammography (DM) images in the cranio-caudal (CC) view from 297 women, 154 without breast cancer and 143 with unilateral breast cancer, were analyzed. Menopause status was used as a surrogate of cumulative endogenous hormonal exposure. Parenchymal texture features were extracted and mammographic percent density (MD%) was computed using validated computerized methods. Univariate and multivariable logistic regression analysis was performed to assess the association between texture features and menopause status, after adjusting for MD% and hormonally-related confounders. The receiver operating characteristic (ROC) area under the curve (AUC) of each model was estimated to evaluate the degree of association between the extracted mammographic features and menopause status.
Coarseness, gray-level correlation, and fractal dimension texture features, have a significant independent association with menopause status in the cancer-affected population; skewness and fractal dimension exhibit a similar association in the cancer-free population (p<0.05). The ROC AUC of the logistic regression model including all texture features was 0.70 (p<0.05) for cancer-affected and 0.63 (p<0.05) for cancer-free women. Texture features retained significant association with menopause status (p<0.05) after adjusting for MD%, age at menarche, ethnicity, contraception use, HRT, parity and age at first birth.
Mammographic texture patterns may reflect the effect of endogenous hormonal exposure on the breast tissue and may capture such effects beyond mammographic density. Differences in texture features between pre- and post-menopausal women are more pronounced in the cancer-affected population, which may be attributed to an increased association to breast cancer risk. Texture features could ultimately be incorporated in breast cancer risk assessment models as markers of hormonal exposure.
Digital mammography; parenchymal texture; hormonal exposure; breast cancer risk
Rationale and Objectives
Mammographic breast density, a strong risk factor for breast cancer, may be measured as either a relative percentage of dense (ie, radiopaque) breast tissue or as an absolute area from either raw (ie, “for processing”) or vendor postprocessed (ie, “for presentation”) digital mammograms. Given the increasing interest in the incorporation of mammographic density in breast cancer risk assessment, the purpose of this study is to determine the inherent reader variability in breast density assessment from raw and vendor-processed digital mammograms, because inconsistent estimates could to lead to misclassification of an individual woman’s risk for breast cancer.
Materials and Methods
Bilateral, mediolateral-oblique view, raw, and processed digital mammograms of 81 women were retrospectively collected for this study (N = 324 images). Mammographic percent density and absolute dense tissue area estimates for each image were obtained from two radiologists using a validated, interactive software tool.
The variability of interreader agreement was not found to be affected by the image presentation style (ie, raw or processed, F-test: P > .5). Interreader estimates of relative and absolute breast density are strongly correlated (Pearson r > 0.84, P < .001) but systematically different (t-test, P < .001) between the two readers.
Our results show that mammographic density may be assessed with equal reliability from either raw or vendor postprocessed images. Furthermore, our results suggest that the primary source of density variability comes from the subjectivity of the individual reader in assessing the absolute amount of dense tissue present in the breast, indicating the need to use standardized tools to mitigate this effect.
Digital mammography; breast density; reader variability; breast cancer risk
Breast cancer risk prediction remains imperfect, particularly among non-white populations. This study examines the impact of including single nucleotide polymorphism (SNP) alleles in risk prediction for white and African American women undergoing screening mammogram. Using a prospective cohort study, standard risk information and buccal swabs were collected at the time of screening mammography. A 12 SNP panel was performed by deCODE Genetics. Five-year and lifetime risks incorporating SNPs were calculated by multiplying estimated Breast Cancer Risk Assessment Tool (BCRAT) risk by the total genetic risk ratio. Concordance between the BCRAT and the Combined Model (BCRAT + SNPs) in identifying high-risk women was measured using the kappa statistic. SNP data were available for 813 women (39% African American, 55% white). The mean BCRAT 5-year risk was 1.70% for whites and 1.19% for African Americans. Mean genetic risk ratios were 1.10 in whites and 1.29 in African Americans. Among whites, three SNPs had higher frequencies, and among African Americans, seven SNPs had higher and four had lower high-risk allele frequencies than previously reported. Agreement between the BCRAT and the Combined Model was relatively low for identifying high-risk women (5-year κ=0.53, lifetime κ=0.37). Addition of SNPs had the greatest effect among African Americans, with 13% identified as having high 5-year risk by BCRAT, but 33% by the Combined Model. A greater proportion of African Americans were reclassified as having high 5-year risk than whites using the Combined Model (21% vs. 10%). The addition of SNPs to the BCRAT reclassifies the high-risk status of some women undergoing screening mammography, particularly African Americans. Further research is needed to determine the clinical validity and utility of the SNP panel for use in breast cancer risk prediction, particularly among African Americans for whom these risk alleles have generally not been validated.
breast cancer; SNPs; risk prediction; African American; race
Survival data can contain an unknown fraction of subjects who are “cured” in the sense of not being at risk of failure. We describe such data with cure-mixture models, which separately model cure status and the hazard of failure among non-cured subjects. No diagnostic currently exists for evaluating the fit of such models; the popular Schoenfeld residual (Schoenfeld, 1982. Partial residuals for the proportional hazards regression-model. Biometrika
69, 239–241) is not applicable to data with cures. In this article, we propose a pseudo-residual, modeled on Schoenfeld's, to assess the fit of the survival regression in the non-cured fraction. Unlike Schoenfeld's approach, which tests the validity of the proportional hazards (PH) assumption, our method uses the full hazard and is thus also applicable to non-PH models. We derive the asymptotic distribution of the residuals and evaluate their performance by simulation in a range of parametric models. We apply our approach to data from a smoking cessation drug trial.
Accelerated failure time; Long-term survivors; Proportional hazards; Residual analysis
Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening.
To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy.
The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011.
Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BIRADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold.
The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors.
It is important to investigate whether genetic susceptible variants exercise the same effects in populations that are differentially exposed to environmental risk factors. Here, we assess the power of four two-phase case-control design strategies for assessing multiplicative gene-environment (G-E) interactions or for assessing genetic or environmental effects in the presence of G-E interactions. With a di-allelic SNP and a binary E, we obtained closed-form maximum likelihood estimates of both main effect and interaction odds ratio parameters under the constraints of G-E independence and Hardy-Weinberg Equilibrium, and used the Wald statistic for all tests. We concluded that i) for testing G-E interactions or genetic effects in the presence of G-E interactions when data for E is fully available, it is preferable to ascertain data for G in a subsample of cases with similar numbers of exposed and unexposed and a random subsample of controls; and ii) for testing G-E interactions or environmental effects in the presence of G-E interactions when data for G is fully available, it is preferable to ascertain data for E in a subsample of cases that has similar numbers for each genotype and a random subsample of controls. In addition, supplementing external control data to an existing casecontrol sample leads to improved power for assessing effects of G or E in the presence of G-E interactions.
Gene-environment interaction; Gene-environment independence; Hardy-Weinberg equilibrium; Retrospective maximum likelihood; Two-phase design
Cancer causes significant symptom burden and diminished quality of life. Despite the expansion of supportive and palliative care services (SPCS), little is known about rates of utilization and barriers to access to these services among oncology outpatients.
We performed a cross-sectional survey in three outpatient medical oncology clinics. Patients with a diagnosis of breast, lung, or gastrointestinal (GI) cancer and a Karnofsky score of ≥60 were included. Patients reported their use of SPCS and any perceived barriers. Multivariable logistic regression was used to identify factors associated with SPCS use.
Among 313 participants, (50.5%) had not used SPCS since cancer diagnosis. The most common services used were nutrition (26.5%), psychiatric/psychological counseling (29.7%), and physical therapy (15.1%). Pain/palliative care and cancer rehabilitation consultations were used by 8.5% and 4.1% of participants, respectively. In multivariate analysis, graduate education was associated with greater SPCS use (adjusted odds ratio [AOR] 2.14, 95% confidence interval [CI] 1.08-4.26) compared with those with high school or less, whereas having lung cancer was associated with less SPCS use (AOR 0.48, 95% CI 0.24-0.96) when compared with those having breast cancer. The biggest reported barriers to using SPCS were a lack of awareness (22.4%) and lack of physician referral (23%).
Approximately half of these patients had not accessed SPCS since cancer diagnosis and cite lack of awareness and physician nonreferral as barriers. Further research is needed to understand patients' needs and beliefs regarding SPCS, and how to integrate SPCS into conventional treatments to improve cancer care.
Next-generation sequencing technology provides an unprecedented opportunity to identify rare susceptibility variants. It is not yet financially feasible to perform whole-genome sequencing on a large number of subjects, and a two-stage design has been advocated to be a practical option. In stage I, variants are discovered by sequencing the whole genomes of a small number of carefully selected individuals. In stage II, the discovered variants of a large number of individuals are genotyped to assess association. Individuals with extreme phenotypes are typically selected in stage I. Using simulated data for unrelated individuals, we explore two important aspects of this two-stage design: the efficiency of discovering common and rare single-nucleotide polymorphisms (SNPs) in stage I and the impact of incomplete SNP discovery in stage I on the power of testing associations in stage II. We apply a sum test and a sum of squared score test for gene-based association analyses evaluating the power of the two-stage design. We obtained the following results from extensive simulation studies and analysis of the GAW17 dataset. When individuals with trait values more extreme than the 99.7 to 99th quantile are included in stage I, the two-stage design could achieve the same as or even higher power than one-stage design if the rare causal variants have large effect sizes. In such tests, fewer than half of the total SNPs including more than half of the causal SNPs were discovered, which included nearly all SNPs with minor allele frequencies (MAFs) ≥ 5%, more than half of the SNPs with MAFs between 1% and 5%, and fewer than half of the SNPs with MAFs <1%. Although a one-stage design may be preferable to identify multiple rare variants having small to moderate effect sizes, our observations support using the two-stage design as a cost-effective option for next-generation sequencing studies.
Two-stage design; Next-generation sequencing; SNP discovery; Rare variants
Recent studies suggest that polymorphisms in genes encoding enzymes involved in drug detoxification and metabolism may influence disease outcome in pediatric acute lymphoblastic leukemia (ALL). We sought to extend current knowledge by using standard and novel statistical methodology to examine polymorphic variants of genes and relapse risk, toxicity, and drug dose delivery in standard risk ALL.
We genotyped and abstracted chemotherapy drug dose data from treatment roadmaps on 557 patients on the Children’s Cancer Group ALL study, CCG-1891. Fourteen common polymorphisms in genes involved in folate metabolism and/or phase I and II drug detoxification were evaluated individually and clique-finding methodology was employed for detection of significant gene-gene interactions.
After controlling for known risk factors, polymorphisms in four genes: GSTP1*B (HR=1.94, p=0.047), MTHFR (HR=1.61, p=0.034), MTRR (HR=1.95, p=0.01), and TS (3R/4R, HR=3.69, p=0.007), were found to significantly increase relapse risk. One gene-gene pair, MTRR A/G and GSTM1 null genotype, significantly increased the risk of relapse after correction for multiple comparisons (p=0.012). Multiple polymorphisms were associated with various toxicities and there was no significant difference in dose of chemotherapy delivered by genotypes.
These data suggest that various polymorphisms play a role in relapse risk and toxicity during childhood ALL therapy and that genotype does not play a role in adjustment of drug dose administered. Additionally, gene-gene interactions may increase the risk of relapse in childhood ALL and the clique method may have utility in further exploring these interactions. childhood ALL therapy.
genotype; acute lymphoblastic leukemia; prognosis; toxicity; gene-gene interactions
Acute rejection (AR) is associated with worse renal allograft outcomes. Therefore, this study investigated single nucleotide polymorphisms (SNPs) to identify genetic variants associated with AR, accounting for center variation, in a multi-center, prospective, observation study.
We enrolled patients from 6 transplant centers, 5 in the U.S. and one in Canada. A total of 2,724 SNPs were genotyped. We accounted for center variation in AR rates by stratifying by transplant center and using novel knowledge discovery methods.
There was significant center variation in AR rates across the six transplant sites. (p<0.0001) Accounting for this difference and clinical factors independently associated with AR, we identified 15 novel SNPs associated with AR with stratification by transplant center (p<0.05). We also identified 15 novel SNPs associated with severity of tubulitis scores, after adjusting for transplant center and other clinical factors independently associated with severity of tubulitis. (p<0.05) There was some overlap with one SNP associated with AR and also associated with severity of tubulitis, among the top 15 SNPs.
Center-to-center variation is a major challenge to genomic studies focused on AR. The SNPs associated with AR and severity of tubulitis in this study, will need to be validated in independent cohort of kidney transplant recipients.
single nucleotide polyrmorphisms; acute rejection; tubulitis
We consider the problem of high-dimensional regression under non-constant error variances. Despite being a common phenomenon in biological applications, heteroscedasticity has, so far, been largely ignored in high-dimensional analysis of genomic data sets. We propose a new methodology that allows non-constant error variances for high-dimensional estimation and model selection. Our method incorporates heteroscedasticity by simultaneously modeling both the mean and variance components via a novel doubly regularized approach. Extensive Monte Carlo simulations indicate that our proposed procedure can result in better estimation and variable selection than existing methods when heteroscedasticity arises from the presence of predictors explaining error variances and outliers. Further, we demonstrate the presence of heteroscedasticity in and apply our method to an expression quantitative trait loci (eQTLs) study of 112 yeast segregants. The new procedure can automatically account for heteroscedasticity in identifying the eQTLs that are associated with gene expression variations and lead to smaller prediction errors. These results demonstrate the importance of considering heteroscedasticity in eQTL data analysis.
Generalized least squares; Heteroscedasticity; Large p small n; Model selection; Sparse regression; Variance estimation
Adenosine (Ade) is an antiepileptic agent. In order to investigate the possible mechanism of action of Ade, its effect on calcium (Ca2+) oscillations in hippocampal neurons of Sprague Dawley (SD) rats was explored. Primary hippocampal neurons were cultured from suckling neonatal SD rats. Cells were cultured for 7–9 days and the Ca2+ oscillations in response to perfusion with Ade were detected using confocal laser scanning microscopy in combination with Fluo-3/AM labeling. This study found that Ade inhibits the spontaneous synchronized Ca2+ oscillation frequency and amplitude in mature hippocampal neurons and such inhibition depends on the Ade dosage level to a certain extent. Ade also had a significant inhibitory effect on high potassium-induced Ca2+ oscillation frequency and amplitude. Ade had a significant inhibitory effect on high-voltage-activated Ca2+ channel-mediated Ca2+ influx and Ca2+ oscillations in neurons. This may be one of the mechanisms for Ade to exert antiepileptic effects as an endogenous substance.
adenosine; confocal laser scanning microscope; calcium oscillation
Mannan-binding lectin (MBL), a lectin homologous to C1q, greatly facilitates C3/C4-mediated opsonophagocytosis of Candida albicans (C. albicans) by human neutrophils, and has the capacity to bind to CR1 (CD35) expressed on circulating neutrophils. The intracellular pool of neutrophil Dectin-1 plays a critical role in stimulating the reactive oxygen species (ROS) generation through recognition of β-1,3-glucan component of phagocytized zymosan or yeasts. However, little is known about whether MBL can mediate the opsonophagocytosis of Candida albicans by neutrophils independent of complement activation, and whether MBL-mediated opsonophagocytosis influence the intracellular expression of Dectin-1 and ROS production. Here we showed that the inhibited phagocytic efficiency of neutrophils as a result of blockage of Dectin-1 was compensated by exogenous MBL alone in a dose-dependent manner. Furthermore, the expressions of Dectin-1 at mRNA and intracellular protein levels were significantly up-regulated in neutrophils stimulated by MBL-pre-incubated C. albicans, while the expression of surface Dectin-1 remained almost unchanged. Nevertheless, the stimulated ROS production in neutrophils was partly and irreversibly inhibited by blockage of Dectin-1 in the presence of exogenous MBL. Confocal microscopy examination showed that intracellular Dectin-1 was recruited and co-distributed with ROS on the surface of some phagocytized yeasts. The β-1,3-glucanase digestion test further suggested that the specific recognition and binding site of human Dectin-1 is just the β-1,3-glucan moiety on the cell wall of C. albicans. These data demonstrate that MBL has an ability to mediate the opsonophagocytosis of Candida albicans by human neutrophils independent of complement activation, which is coupled with intracellular Dectin-1-triggered ROS production.
To estimate whether African ancestry, specific gene polymorphisms, and gene-environment interactions could account for some of the unexplained preterm birth variance within blacks.
We genotyped 1,509 African ancestry informative markers, cytochrome P-450 1A1 (CYP1A1) and glutathione S-transferases Theta 1 (GSTT1) variants in 1,030 self-reported black mothers. We estimated the African ancestral proportion using the ancestry informative markers for all 1,030 self-reported black mothers. We examined the effect of African ancestry and CYP1A1 and GSTT1 smoking interactions on preterm birth cases as a whole and within its subgroups: very preterm birth (gestational age less than 34 weeks); and late preterm birth (gestational age greater than 34 and less than 37 weeks). We applied logistic regression and receiver operating characteristic (ROC) curve analysis, separately, to evaluate if African ancestry and CYP1A1- and GSTT1-smoking interactions could make additional contributions to preterm birth beyond epidemiological factors.
We found significant associations of African ancestry with preterm birth (22% vs. 31%, OR=1.11; 95%CI: 1.02–1.20) and very preterm birth (23% vs. 33%, OR=1.17; 95%CI: 1.03–1.33), but not with late preterm birth (22% vs. 29%, OR=1.06; 95%CI: 0.97–1.16). In addition, the ROC curve analysis suggested that African ancestry and CYP1A1- and GSTT1-smoking interactions made substantial contributions to very preterm birth beyond epidemiologic factors.
Our data underscore the importance of simultaneously considering epidemiological factors, African ancestry, specific gene polymorphisms and gene-environment interactions to better understand preterm birth racial disparity and to improve our ability to predict preterm birth, especially very preterm birth.